def objective(p0, q, Iq, sd, y, Wy, Nr, weighted):
j1, j2, j3, j4, j5, j6, j7, evi = iftv2(exp(p0[0]), p0[1], q, Iq,
sd, Nr, y, Wy, weighted,
smeared)
return -evi
def objective(p0,q,Iq,sd,y,Wy,Nr,weighted):
j1,j2,j3,j4,j5,j6,j7,evi = iftv2(exp(p0[0]),p0[1],q,Iq,sd,Nr,y,Wy,weighted,smeared)
return -evi
def runift(self):
# obtain parameters for solving P(r)
self.getiftparams()
# run fortran wrapper ift from iftreg module
alpha = exp(self.data.logalpha)
Dmax = self.data.Dmax
Nr = self.data.Nr
if self.data.smeared:
y = self.y
Wy = self.Wy
else:
y = None
Wy = None
q = self.data.q
Iq = self.data.Iq
sd = self.data.sd
qmin_id = self.data.datarange[0]
qmax_id = self.data.datarange[1]
q = q[qmin_id:qmax_id]
Iq = Iq[qmin_id:qmax_id]
sd = sd[qmin_id:qmax_id]
# solve once for extrapolation
Jreg, Ireg, Jreg_extrap, Ireg_extrap, q_full, r, pr, evi = iftv2(
alpha, Dmax, q, Iq, sd, Nr, y, Wy, self.weighdata,
self.data.smeared)
r = linspace(0.0, Dmax, Nr)
# do resampling to estimate error in P(r)
Nerr = 100
Pr_list = zeros((Nerr, Nr))
# precompute smearing matrix
if self.data.smeared:
K = trans(q, r, y=y, Wy=Wy)
Kunsmeared = trans(q, r)
else:
K = trans(q, r)
Kunsmeared = K * 1.0
for n in range(Nerr):
wrkiq = normal(loc=Iq, scale=sd)
_, _, r, wrkpr, _ = iftv3(K, Kunsmeared, alpha, Dmax, q, wrkiq, sd,
Nr, self.weighdata, self.data.smeared)
Pr_list[n, :] = wrkpr
# assign full angular range to data
self.data.q_full = q_full
# calculate chi-square for fit
chi = ((Iq - Jreg)**2 / sd**2).mean()
# assign solution to saxsdata
pr, pr_error = Pr_list.mean(axis=0), Pr_list.std(axis=0)
self.data.r = r
self.data.pr = pr
self.data.pr_error = pr_error
self.data.Jreg = Jreg
self.data.Ireg = Ireg
self.data.solved = True
# calculate real-space Rg, etc.
dr = r[1] - r[0]
Rg_sq = 0.5 * (pr * r**2 * dr).sum() / (pr * dr).sum()
self.data.Rg = sqrt(Rg_sq)
# Calculate SAXSMoW ...
dq = q[1] - q[0]
Iq_q = Ireg_extrap * q_full
# Integrate using Trapezoid rule
#Qinv = 0.5*dq * (Iq_q[0] + Iq_q[-1] + 2*(Iq_q[1:-1]).sum())
# Integrate using Simpsons's rule
Qinv = simps(y=Iq_q, x=q_full)
Vc = Ireg_extrap[0] / Qinv
ramboratio = Vc**2 / self.data.Rg
# from saxsmow calibration
# slope, offset = saxsmow(q.max())
# using protein density 1.35 g/mL
# 1.35 g/cm^3 x 10^-3 kg/g x 1 Dalton/1.66e-27kg x 1cm^3/1e24 Angstrom^3
# is 0.81325 Dalton/Angstrom^3
#saxsmw = (slope*appVol + offset) * 0.81325e-3
# Using the numbers from biosis.net (Robert Rambo)
# ONLY for protein / complexes, will have to be different for nucleic acid / complex
saxsmw = (ramboratio / 0.1231)**1.0
saxsmw = saxsmw * 0.001
chi_str = "Chi^2: {0:5.3f}\nI(0): {1:8.3E}\nM.W.: {2:4.1f}kDa".format(
chi, Ireg_extrap[0], saxsmw)
chi_textitem = TextItem(chi_str, color='k')
chi_textitem.setPos(q.max() * 0.65, Ireg_extrap[0] * 0.9)
chi_textitem.savex = q.max() * 0.65
chi_textitem.savey = Ireg_extrap[0] * 0.9
realRg_textitem = pg.TextItem("Rg_real : {0:7.2f}".format(sqrt(Rg_sq)),
color='k',
anchor=(0, 0))
realRg_textitem.setPos(r.max() * 0.6, pr.max() * 0.9)
#realpars_textitem = pg.TextItem()
self.prplot.clear()
self.rawplot.clear()
self.rawplot.addItem(self.Iq_errorbar)
self.rawplot.plot(q, Iq, pen=self.redpen)
self.rawplot.plot(q_full, Jreg_extrap, pen=self.blackpen)
# only plot the unsmeared (Ireg) when data is smeared
# otherwise the lines will just overlay and looks ugly
if self.data.smeared:
self.rawplot.plot(q_full, Ireg_extrap, pen=self.bluepen)
self.rawplot.addItem(chi_textitem)
self.prplot.addItem(realRg_textitem)
# add P(r) error bars
pr_errorbar = ErrorBarItem(x=r,
y=pr,
height=pr_error,
beam=0,
pen={
'color': '#a9a9a9',
'width': 2
})
self.prplot.addItem(pr_errorbar)
self.prplot.plot(r, pr, pen=self.redpen)
# after solving, plot the unsmeared normalized kratky plot
# x = q*Rg
# y = (q*Rg)^2 * I(q)/I(0)
xc_kratky = q_full * self.data.Rg
yc_kratky = (xc_kratky)**2 * Jreg_extrap / Jreg_extrap[0]
# update the raw data with the Real Space Rg
x_kratky = q * self.data.Rg
y_kratky = (x_kratky)**2 * Iq / Jreg_extrap[0]
yerr_kratky = (x_kratky)**2 * sd / Jreg_extrap[0]
kratky_errorbar_corr = ErrorBarItem(x=x_kratky,y=y_kratky,height=yerr_kratky,\
beam=0,pen={'color':'#a9a9a9','width':2})
self.kratkyplot.clear()
self.kratkyplot.addItem(kratky_errorbar_corr)
self.kratkyplot.plot(x_kratky, y_kratky, pen=self.redpen)
self.kratkyplot.setLabel('bottom', 'q x Rg')
self.kratkyplot.setLabel('left', '(q*Rg)<sup>2</sup> x I(q)/I(0)')
self.kratkyplot.addLine(x=1.73, pen=self.graydashedpen)
self.kratkyplot.plot(xc_kratky, yc_kratky, pen=self.blackpen)
# save file (if user wants to)
savefile = int(self.saveiftresult.checkState())
if savefile > 0:
fn_out = str(self.iftresultout.text())
# integrate P(r) to 1
dr = r[1] - r[0]
prscale = pr.sum() * dr
pr = pr / prscale
pr_error = pr_error / prscale
fhd_out = open(fn_out, "wt")
for n in range(Nr):
fhd_out.write("{0:5.2f} {1:6.4E} {2:6.4E}\n".format(
r[n], pr[n], pr_error[n]))
fhd_out.close()
print("saved to {0}".format(fn_out))
def runift(self):
# obtain parameters for solving P(r)
self.getiftparams()
# run fortran wrapper ift from iftreg module
alpha = exp(self.data.logalpha)
Dmax = self.data.Dmax
Nr = self.data.Nr
if self.data.smeared:
y = self.y
Wy = self.Wy
else:
y = None
Wy = None
q = self.data.q
Iq = self.data.Iq
sd = self.data.sd
qmin_id = self.data.datarange[0]
qmax_id = self.data.datarange[1]
q = q[qmin_id:qmax_id]
Iq = Iq[qmin_id:qmax_id]
sd = sd[qmin_id:qmax_id]
Jreg,Ireg, Jreg_extrap, Ireg_extrap, q_full, r,pr,evi = iftv2(alpha,Dmax,q,Iq,sd,Nr,y,Wy,self.weighdata,self.data.smeared)
# assign full angular range to data
self.data.q_full = q_full
# calculate chi-square for fit
chi = ((Iq-Jreg)**2/sd**2).mean()
# assign solution to saxsdata
self.data.r, self.data.pr = r, pr
self.data.Jreg = Jreg
self.data.Ireg = Ireg
self.data.solved = True
# calculate real-space Rg, etc.
dr = r[1]-r[0]
Rg_sq = 0.5 * (pr * r**2 * dr).sum() / (pr*dr).sum()
self.data.Rg = sqrt(Rg_sq)
# Calculate SAXSMoW ...
dq = q[1]-q[0]
Iq_q = Ireg_extrap * q_full
# Integrate using Trapezoid rule
#Qinv = 0.5*dq * (Iq_q[0] + Iq_q[-1] + 2*(Iq_q[1:-1]).sum())
# Integrate using Simpsons's rule
Qinv = simps(y=Iq_q,x=q_full)
Vc = Ireg_extrap[0]/Qinv
ramboratio = Vc**2 / self.data.Rg
# from saxsmow calibration
# slope, offset = saxsmow(q.max())
# using protein density 1.35 g/mL
# 1.35 g/cm^3 x 10^-3 kg/g x 1 Dalton/1.66e-27kg x 1cm^3/1e24 Angstrom^3
# is 0.81325 Dalton/Angstrom^3
#saxsmw = (slope*appVol + offset) * 0.81325e-3
# Using the numbers from biosis.net (Robert Rambo)
# ONLY for protein / complexes, will have to be different for nucleic acid / complex
saxsmw = (ramboratio/0.1231)**1.0
saxsmw = saxsmw * 0.001
chi_str = "Chi^2: {0:5.3f}\nI(0): {1:8.3E}\nM.W.: {2:4.1f}kDa".format(chi,Ireg_extrap[0],saxsmw)
chi_textitem = TextItem(chi_str, color='k')
chi_textitem.setPos(q.max()*0.65,Ireg_extrap[0]*0.9)
chi_textitem.savex = q.max()*0.65
chi_textitem.savey = Ireg_extrap[0]*0.9
realRg_textitem = pg.TextItem("Rg_real : {0:7.2f}".format(sqrt(Rg_sq)), color='k', anchor=(0,0))
realRg_textitem.setPos(r.max()*0.6,pr.max()*0.9)
#realpars_textitem = pg.TextItem()
self.prplot.clear()
self.rawplot.clear()
self.rawplot.addItem(self.Iq_errorbar)
self.rawplot.plot(q,Iq,pen=self.redpen)
self.rawplot.plot(q_full,Jreg_extrap,pen=self.blackpen)
# only plot the unsmeared (Ireg) when data is smeared
# otherwise the lines will just overlay and looks ugly
if self.data.smeared:
self.rawplot.plot(q_full,Ireg_extrap,pen=self.bluepen)
self.rawplot.addItem(chi_textitem)
self.prplot.addItem(realRg_textitem)
self.prplot.plot(r,pr,pen=self.redpen)
# after solving, plot the unsmeared normalized kratky plot
# x = q*Rg
# y = (q*Rg)^2 * I(q)/I(0)
xc_kratky = q_full * self.data.Rg
yc_kratky = (xc_kratky)**2 * Jreg_extrap/Jreg_extrap[0]
# update the raw data with the Real Space Rg
x_kratky = q * self.data.Rg
y_kratky = (x_kratky)**2 * Iq/Jreg_extrap[0]
self.kratkyplot.clear()
self.kratkyplot.plot(x_kratky, y_kratky, pen=self.redpen)
self.kratkyplot.setLabel('bottom','q x Rg')
self.kratkyplot.setLabel('left','(q*Rg)<sup>2</sup> x I(q)/I(0)')
self.kratkyplot.addLine(x=1.73,pen=self.graydashedpen)
self.kratkyplot.plot(xc_kratky, yc_kratky, pen=self.blackpen)
# save file (if user wants to)
savefile = int(self.saveiftresult.checkState())
if savefile>0:
fn_out = str(self.iftresultout.text())
# integrate P(r) to 1
dr = r[1]-r[0]
pr = pr/(pr.sum()*dr)
fhd_out = open(fn_out,"wt")
for n in range(Nr):
fhd_out.write("{0:5.2f} {1:6.2E}\n".format(r[n],pr[n]))
fhd_out.close()
print "saved to {0}".format(fn_out)